EGU General Assembly 2016

1. Data-driven behavioural modelling of residential water
consumption to inform water demand management strategies
M. Giuliani, A. Cominola, A. Alsahaf, A. Castelletti, M. Anda
EGU General Assembly 2016

2. US
246.2
Urban population in millions
81%
Urban percentage
Mexico
84.392
77%
Colombia
34.3
73%
Brazil
162.6
85%
Argentina
35.6
90%
Ukraine
30.9
68%
Russia
103.6
73%
China
559.2Urban population in millions
42%Urban percentage
Turkey
51.1
68%
India
329.3
29%
Bangladesh
38.2
26%
Philippines
55.0
64%
Indonesia
114.1
50%
S Korea
39.0
81%
Japan
84.7
66%
Egypt
33.1
43%
S Africa
28.6
60%
Canada
26.3
Venezuela
26.0
Poland
23.9
Thailand
21.5
Australia
18.3
Netherlands
13.3
Peru
21.0
Saudi Arabia
20.9
Iraq
20.3
Vietnam
23.3
DR Congo
20.2
Algeria
22.0Morocco
19.4
Malaysia
18.1
Burma
16.5
Sudan
16.3
Chile
14.6
N Korea
14.1
Ethiopia
13.0
Uzbekistan
10.1
Tanzania
9.9
Romania
11.6
Ghana
11.3
Syria
10.2
Belgium
10.2
80%
94%
62%
33%
89%
81%
73%
81%
67%
27%
33%
65%
60%
69%
32%
43%
88%
62%
16%
37%
25%
54%
49%
51%
97%
Nigeria
68.6
50%
UK
54.0
90%
France
46.9
77%
Spain
33.6
77%
Italy
39.6
68%
Germany
62.0
75%
Iran
48.4
68%
Pakistan
59.3
36%
Cameroon
Angola
Ecuador
Ivory
Coast
Kazakh-
stan
Cuba
Afghan-
istan
Sweden
Kenya
Czech
Republic
9.5
9.3
8.7
8.6
8.6
8.5
7.8
7.6
7.6
7.4
Mozam-
bique
Hong
Kong
Belarus
Tunisia
Hungary
Greece
Israel
Guate-
mala
Portugal
Yemen
Dominican
Republic
Bolivia
Serbia &
Mont
Switzer-
land
Austria
Bulgaria
Mada-
gascar
Libya
Senegal
Jordan
Zimbabwe
Nepal
Denmark
Mali
Azerbaijan
Singapore
El
Salvador
Zambia
Uganda
Puerto
Rico
Paraguay
UAE
Benin
Norway
New
Zealand
Honduras
Haiti
Nicaragua
Guinea
Finland
Uruguay
Lebanon
Somalia
Sri Lanka
Cambodia
Slovakia
Costa Rica
Palestine
Kuwait
Togo
Chad
Burkina
Ireland
Croatia
Congo
Niger
Sierra Leone
Malawi
Panama
Turkmenistan
Georgia
Lithuania
Liberia
Moldova
Rwanda
Kyrgyzstan
Oman
Armenia
Bosnia
Tajikistan
CAR
Melanesia
Latvia
Mongolia
Albania
Jamaica
Macedonia
Mauritania Laos
Gabon
Botswana
Slovenia
Eritrea
Estonia
Gambia
Burundi
Papua New Guinea
Namibia
Mauritius
Guinea-Bissau
Lesotho E Timor
Bhutan
Swaziland
Trinidad & Tobago
The earth reaches a momentous
milestone: by next year, for the first time
in history, more than half its population
will be living in cities. Those 3.3 billion
people are expected to grow to 5 billion
by 2030 — this unique map of the world
shows where those people live now
At the beginning of the 20th
century, the world's urban
population was only 220
million, mainly in the west
By 2030, the towns and
cities of the developing
world will make up 80%
of urban humanity
The new urban world
Urban growth, 2005—2010
Predominantly urban
75% or over
Predominantly urban
50—74%
Predominantly rural
25—49% urban
Predominantly rural
0—24% urban
Cities over 10 million people
(greater urban area)
Key
Tokyo
33.4
Osaka
16.6
Seoul
23.2
Manila
15.4
Jakarta
14.9
Dacca
13.8
Bombay
21.3
Delhi
21.1 Calcutta
15.5
Karachi
14.8
Shanghai
17.3
Canton
14.5
Beijing
12.7
Moscow
13.4
Tehran
12.1
Cairo
15.9
Istanbul
11.7
London
12.0
Lagos
10.0
Mexico
City
22.1
New York
21.8
Sao Paulo
20.4
LA
17.9
Rio de
Janeiro
12.2
Buenos
Aires
13.5
3,307,950,000The world’s urban population — from a total of 6,615.9 million SOURCE: UNFPA GRAPHIC: PAUL SCRUTONAfrica Asia Oceania Europe
0.1%
Eastern Europe
-0.4%
Arab States
Latin America
& Caribbean North America
3.2%
2.4%
1.3%
2.8%
1.7%
1.3%
Urban population is growing
Source: United Nations Population Fund, 2007

3. 2000 2030 2050
+130%
Domesticwaterdemand
41 megacities
worldwide
Source: United Nations. Department of Economic and Social Affairs. Population Division, 2010
Leflaive, X., et al. (2012), "Water", in OECD, OECD Environmental Outlook to 2050: The Consequences of Inaction, OECD Publishing, Paris
… and so residential water demand

4. city/district scale
Water demand management strategies
TECHNOLOGICAL (e.g., water efficient devices)
FINANCIAL (e.g., water price schemes, incentives)
LEGISLATIVE (e.g., water usage restrictions)
OPERATION & MAINTENANCE (e.g., leak detection)
EDUCATION (e.g., water awareness campaigns, workshops)

5. city/district scale
Water demand management strategies
TECHNOLOGICAL (e.g., water efficient devices)
FINANCIAL (e.g., water price schemes, incentives)
LEGISLATIVE (e.g., water usage restrictions)
OPERATION & MAINTENANCE (e.g., leak detection)
EDUCATION (e.g., water awareness campaigns, workshops)
customized WDMS

6. What is the current state-of-the-art
of residential Water Demand Management?

7. 1990
1994
50
30
10
1995
1999
2000
2004
2005
2009
2010
2015
Benefits and challenges of using smart meters for advancing
residential water demand modeling and management: A review
A. Cominola a
, M. Giuliani a
, D. Piga b
, A. Castelletti a, c, *
, A.E. Rizzoli d
a
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
b
IMT Institute for Advanced Studies Lucca, Lucca, Italy
c
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
d
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, SUPSI-USI, Lugano, Switzerland
a r t i c l e i n f o
Article history:
Received 2 April 2015
Received in revised form
21 July 2015
Accepted 21 July 2015
Available online xxx
Keywords:
Smart meter
Residential water management
Water demand modeling
Water conservation
a b s t r a c t
Over the last two decades, water smart metering programs have been launched in a number of medium
to large cities worldwide to nearly continuously monitor water consumption at the single household
level. The availability of data at such very high spatial and temporal resolution advanced the ability in
characterizing, modeling, and, ultimately, designing user-oriented residential water demand manage-
ment strategies. Research to date has been focusing on one or more of these aspects but with limited
integration between the specialized methodologies developed so far. This manuscript is the first
comprehensive review of the literature in this quickly evolving water research domain. The paper
contributes a general framework for the classification of residential water demand modeling studies,
which allows revising consolidated approaches, describing emerging trends, and identifying potential
future developments. In particular, the future challenges posed by growing population demands, con-
strained sources of water supply and climate change impacts are expected to require more and more
integrated procedures for effectively supporting residential water demand modeling and management in
several countries across the world.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
World's urban population is expected to raise from current
54%e66% in 2050 and to further increase as a consequence of the
unlikely stabilization of human population by the end of the cen-
tury (Gerland et al., 2014). By 2030 the number of mega-cities,
namely cities with more than 10 million inhabitants, will grow
over 40 (UNDESA, 2010). This will boost residential water demand
(Cosgrove and Cosgrove, 2012), which nowadays covers a large
portion of the public drinking water supply worldwide (e.g.,
60e80% in Europe (Collins et al., 2009), 58% in the United States
(Kenny et al., 2009)).
The concentration of the water demands of thousands or mil-
lions of people into small areas will considerably raise the stress on
finite supplies of available freshwater (McDonald et al., 2011a).
Besides, climate and land use change will further increase the
number of people facing water shortage (McDonald et al., 2011b). In
such context, water supply expansion through the construction of
new infrastructures might be an option to escape water stress in
some situations. Yet, geographical or financial limitations largely
restrict such options in most countries (McDonald et al., 2014).
Here, acting on the water demand management side through the
promotion of cost-effective water-saving technologies, revised
economic policies, appropriate national and local regulations, and
education represents an alternative strategy for securing reliable
water supply and reduce water utilities' costs (Gleick et al., 2003).
In recent years, a variety of water demand management stra-
tegies (WDMS) has been applied (for a review, see Inman and
Jeffrey, 2006, and references therein). However, the effectiveness
of these WDMS is often context-specific and strongly depends on
our understanding of the drivers inducing people to consume or
save water (Jorgensen et al., 2009). Models that quantitatively
describe how water demand is influenced and varies in relation to
exogenous uncontrolled drivers (e.g., seasonality, climatic condi-
Contents lists available at ScienceDirect
Environmental Modelling & Software
journal homepage: www.elsevier.com/locate/envsoft
Environmental Modelling & Software 72 (2015) 198e214
134 studies over the
last 25 years
A quick journey in the literature

8. 1990
1994
50
30
10
1995
1999
2000
2004
2005
2009
2010
2015
Benefits and challenges of using smart meters for advancing
residential water demand modeling and management: A review
A. Cominola a
, M. Giuliani a
, D. Piga b
, A. Castelletti a, c, *
, A.E. Rizzoli d
a
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
b
IMT Institute for Advanced Studies Lucca, Lucca, Italy
c
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
d
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, SUPSI-USI, Lugano, Switzerland
a r t i c l e i n f o
Article history:
Received 2 April 2015
Received in revised form
21 July 2015
Accepted 21 July 2015
Available online xxx
Keywords:
Smart meter
Residential water management
Water demand modeling
Water conservation
a b s t r a c t
Over the last two decades, water smart metering programs have been launched in a number of medium
to large cities worldwide to nearly continuously monitor water consumption at the single household
level. The availability of data at such very high spatial and temporal resolution advanced the ability in
characterizing, modeling, and, ultimately, designing user-oriented residential water demand manage-
ment strategies. Research to date has been focusing on one or more of these aspects but with limited
integration between the specialized methodologies developed so far. This manuscript is the first
comprehensive review of the literature in this quickly evolving water research domain. The paper
contributes a general framework for the classification of residential water demand modeling studies,
which allows revising consolidated approaches, describing emerging trends, and identifying potential
future developments. In particular, the future challenges posed by growing population demands, con-
strained sources of water supply and climate change impacts are expected to require more and more
integrated procedures for effectively supporting residential water demand modeling and management in
several countries across the world.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
World's urban population is expected to raise from current
54%e66% in 2050 and to further increase as a consequence of the
unlikely stabilization of human population by the end of the cen-
tury (Gerland et al., 2014). By 2030 the number of mega-cities,
namely cities with more than 10 million inhabitants, will grow
over 40 (UNDESA, 2010). This will boost residential water demand
(Cosgrove and Cosgrove, 2012), which nowadays covers a large
portion of the public drinking water supply worldwide (e.g.,
60e80% in Europe (Collins et al., 2009), 58% in the United States
(Kenny et al., 2009)).
The concentration of the water demands of thousands or mil-
lions of people into small areas will considerably raise the stress on
finite supplies of available freshwater (McDonald et al., 2011a).
Besides, climate and land use change will further increase the
number of people facing water shortage (McDonald et al., 2011b). In
such context, water supply expansion through the construction of
new infrastructures might be an option to escape water stress in
some situations. Yet, geographical or financial limitations largely
restrict such options in most countries (McDonald et al., 2014).
Here, acting on the water demand management side through the
promotion of cost-effective water-saving technologies, revised
economic policies, appropriate national and local regulations, and
education represents an alternative strategy for securing reliable
water supply and reduce water utilities' costs (Gleick et al., 2003).
In recent years, a variety of water demand management stra-
tegies (WDMS) has been applied (for a review, see Inman and
Jeffrey, 2006, and references therein). However, the effectiveness
of these WDMS is often context-specific and strongly depends on
our understanding of the drivers inducing people to consume or
save water (Jorgensen et al., 2009). Models that quantitatively
describe how water demand is influenced and varies in relation to
exogenous uncontrolled drivers (e.g., seasonality, climatic condi-
Contents lists available at ScienceDirect
Environmental Modelling & Software
journal homepage: www.elsevier.com/locate/envsoft
Environmental Modelling & Software 72 (2015) 198e214
134 studies over the
last 25 years
A quick journey in the literature
first smart meters
deployment

9. quarterly / half yearly basis readings
1 kilolitre (=1m3)
Traditional water meters
Traditional vs Smart water meters

10. Smart meters resolution: 72 pulses/L
(=72k pulses/m3 )
Data logging resolution: 5-10 s interval
Information on time-of-day for consumption
Smart water meters
Traditional vs Smart water meters

11. 36%
43%
13%
6%
<1%
Smart meters deployment sites worldwide
134 studies over the last 25 years
Cominola et al. (2015), Benefits and challenges of using smart meters for advancing residential water demand
modeling and management: A review, Enviornmental Modelling & Software.

13. CUSTOMIZED DEMAND
MANAGEMENT
CONSUMERS’
COMMUNITY
WATER
CONSUMPTION
MONITORING
BEHAVIORAL USER
MODELLING
_ SMART METERED WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
_ technological
_ financial
_ legislative
_ operation and maintenance
_ education
Smart meters potential for WDMS

14. CUSTOMIZED DEMAND
MANAGEMENT
CONSUMERS’
COMMUNITY
WATER
CONSUMPTION
MONITORING
BEHAVIORAL USER
MODELLING
_ SMART METERED WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
_ technological
_ financial
_ legislative
_ operation and maintenance
_ education
3-step behavioral modelling procedure
SMART METERED
WATER CONSUMPTION
Users’ consumption class
(label)
USERS PROFILING
PREDICTED
CONSUMPTION
PROFILE
HOUSEHOLD and
CONSUMERS’
PSYCHOGRAPHIC DATA
Relevant consumption
determinants subset
BEHAVIORAL MODEL
z
FEATURE EXTRACTION

15. Case Study Application
Source: H2ome smart project (Anda et al., 2013)
Pilbara
Kimberley
3-months resolution
water consumption readings
(Aug 2010 – Feb 2012)
Approx. 730 households
27 user and household features
Dataset
Case study application

16. Dataset
Years of occupancy
House responsibility
# occupants
Resident type
Land use
House type
# toilets
Washing machine type
Toilet type
Shower type
Dishwasher presence
Garden area
Watering method
Watering time
Mulch usage
Native plant presence
Average max temperature
Average min temperature
Average daily precipitation
Pool presence
Pool cover usage
Spa presence
Town
Suburb
Metering period start
Metering period end
Metering period length
Users’ and households’ features

17. USERS PROFILING FEATURE EXTRACTION
Chi-square score
Information Gain
Fast Correlation Based Filter
Correlation Feature Selection
Bayesian Logistic Regression
Sparse Bayesian Multinomial
Logistic Regression
Iterative Input Variable
Selection
Naïve Bayes Classifier
J48 Decision Tree algorithm
Extremely Randomized Trees
BEHAVIORAL MODEL
Cominola et al. (2015), Modelling residential water consumers’ behaviors by feature selection and feature weighting,
In Proceedings of the 36th IAHR world congress
K-means clustering (k=4)
Algorithms

18. USERS PROFILING FEATURE EXTRACTION
Chi-square score
Information Gain
Fast Correlation Based Filter
Correlation Feature Selection
Bayesian Logistic Regression
Sparse Bayesian Multinomial
Logistic Regression
Iterative Input Variable
Selection
Naïve Bayes Classifier
J48 Decision Tree algorithm
Extremely Randomized Trees
BEHAVIORAL MODEL
K-means clustering (k=4)
Algorithms
An evaluation framework for input variable selection algorithms for
environmental data-driven models
Stefano Galelli a, *
, Greer B. Humphrey b
, Holger R. Maier b
, Andrea Castelletti c
,
Graeme C. Dandy b
, Matthew S. Gibbs b, d
a
Pillar of Engineering Systems and Design, Singapore University of Technology and Design, 20 Dover Drive, 138682, Singapore
b
School of Civil, Environmental, and Mining Engineering, University of Adelaide, SA, 5005, Australia
c
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza L. da Vinci, 32, 20133, Milan, Italy
d
Department of Environment, Water and Natural Resources, GPO Box 2384, Adelaide, SA, 5001, Australia
Contents lists available at ScienceDirect
Environmental Modelling & Software
journal homepage: www.elsevier.com/locate/envsoft
Environmental Modelling & Software 62 (2014) 33e51
http://ivs4em.deib.polimi.it/

19. F-score
Relativecontribution
Results: feature extraction

20. Relativecontribution
Fscore
ET Largest Class Random
Accuracy 0.75 0.56 0.44
F-score 0.48 0.18 0.25
# users correctly profiled
total # users
True Positive
True Positive + False Negative
True Positive
True Positive + False Positive
Results: behavioral model

21. Take home points
• Smart-meters can improve our understanding of residential water
consumption behaviors at very high spatial and temporal resolution
• Feature extraction algorithms can identify key users’ features
determining the observed water consumption behaviors
• The combination of smart meters and machine learning techniques
has the potential for supporting the development of data-driven
behavioral models

22. LONDON | UK
Thames Water water supply utility
15 million customers served
2.6 Gl/day drinking water distributed
Development plan: 3 Million smart meters installed by 2030
LOCARNO | CH
Società Elettrica Sopracenerina
power supply utility, 80 thousand
customers served
Interested in multi-utility smart metering
(water, energy, gas)
Almost 400 smart water meters installed
VALENCIA | ES
EMIVASA water supply utility
2 million customers served
490,000 water smart meters currently installed
Development plan: 650,000 water smart meters installed by end 2015
Ongoing research

23. _ technological
_ financial
_ legislative
_ operation and maintenance
_ education
CUSTOMIZED DEMAND
MANAGEMENT
CONSUMERS’
COMMUNITY
WATER
CONSUMPTION
MONITORING
BEHAVIORAL USER
MODELLING
_ SMART METERED WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Hour the day
0 5 10 15 20 25
Normalizedhouseholdconsumption
0
0.05
0.1
0.15
WATER DATA
END_USE ANALYTICS
47%
12%
9%
8%
23%
HPE
CDE CDE CDE C
HPE HPE Hhighest
contribution
lowest
contribution
garden
shower
toilet
faucet
dishwasher
Ongoing research

24. _ technological
_ financial
_ legislative
_ operation and maintenance
_ education
CUSTOMIZED DEMAND
MANAGEMENT
CONSUMERS’
COMMUNITY
WATER
CONSUMPTION
MONITORING
BEHAVIORAL USER
MODELLING
_ SMART METERED WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Ongoing research
CONSUMER PORTAL
ENGAGEMENT AND
BEHAVIOURAL CHANGE

25. Ongoing research

26. Ongoing research

27. Matteo Giuliani
matteo.giuliani@polimi.it
@smartH2Oproject
@NRMPolimi
@MxgTeo
Thank You
The event will be held on August 22-25 in the Monte Verità, Switzerland.
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